code: def evaluate_batch(self, batch: TorchData) -> Dict[str, Any]:
      batch = cast(Tuple[torch.Tensor, torch.Tensor], batch)
      data, labels = batch
  
      with autocast():
          output = self.model(data)
          validation_loss = torch.nn.functional.nll_loss(
              output, labels
          ).item()
  
      pred = output.argmax(dim=1, keepdim=True)
      accuracy = pred.eq(labels.view_as(pred)).sum().item() / len(data)
  
      return {"validation_loss": validation_loss, "accuracy": accuracy}
  
file_path: determined/harness/tests/experiment/fixtures/pytorch_amp/manual_amp_model_def.py
imports: from typing import Any, Dict, Optional, Sequence, Tuple, Union, cast
  import torch
  from torch.cuda.amp import GradScaler, autocast
  from train import MNistTrial
  from determined.pytorch import PyTorchTrialContext
label_desc: Use image node_id 3 for calling the MNistManualAMPTrial obj's underlying member method code with example usage: obj.evaluate_batch(batch) and returns: dict